Bottom Line:
In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters.Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones.The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.

Affiliation: Dept. of Mathematics, University of Houston, Houston, Texas, United States of America.

ABSTRACTAutomated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i) image segmentation based on support vector machines with specially designed multiscale filters; (ii) soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma's surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.

pone.0121886.g005: Image preprocessing.(A) MIP image of a representative neuron from a confocal image stack. (B) Shearlet-based denoised version of the same image. (C) Smoothed version of the denoised image, where the smoothing is obtained by convolving the image with a Gaussian kernel of size 3 × 3 and standard deviation σ = .6

Mentions:
Our preprocessing stage includes a denoising algorithm which is the same shealet-based algorithm described above for the 2D case and is applied to each image of the stack. However, we found that, even after denoising, the level of fluorescent intensity in the images was still rather uneven and this may cause some difficulties in the successive processing stages (e.g., holes in the extracted solid). To mitigate this effect, we applied a simple smoothing filter that is implemented by convolving the image stack with a 3D Gaussian kernel of size 33 and standard deviation σ = .6. The value of σ and the size of the filter were determined after extensive numerical testing to provide the most satisfactory performance for surface detection. Fig. 5 illustrates our preprocessing method on a representative image of a neuron from a 3D confocal stack.

pone.0121886.g005: Image preprocessing.(A) MIP image of a representative neuron from a confocal image stack. (B) Shearlet-based denoised version of the same image. (C) Smoothed version of the denoised image, where the smoothing is obtained by convolving the image with a Gaussian kernel of size 3 × 3 and standard deviation σ = .6

Mentions:
Our preprocessing stage includes a denoising algorithm which is the same shealet-based algorithm described above for the 2D case and is applied to each image of the stack. However, we found that, even after denoising, the level of fluorescent intensity in the images was still rather uneven and this may cause some difficulties in the successive processing stages (e.g., holes in the extracted solid). To mitigate this effect, we applied a simple smoothing filter that is implemented by convolving the image stack with a 3D Gaussian kernel of size 33 and standard deviation σ = .6. The value of σ and the size of the filter were determined after extensive numerical testing to provide the most satisfactory performance for surface detection. Fig. 5 illustrates our preprocessing method on a representative image of a neuron from a 3D confocal stack.

Bottom Line:
In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters.Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones.The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.

Affiliation:
Dept. of Mathematics, University of Houston, Houston, Texas, United States of America.

ABSTRACTAutomated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i) image segmentation based on support vector machines with specially designed multiscale filters; (ii) soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma's surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.